Search clarification in conversational search systems exhibits a clarification pane composed of several candidate aspect items and a clarifying question. To generate a pane, existing studies usually rely on unstructured document texts. However, important structured information in search results is not effectively considered, making the generated panes inaccurate in some cases. In this paper, we emphasize the importance of structured information in search results for improving search clarification. We propose enhancing unstructured documents with two kinds of structured information: one is "In-List'' relation obtained from HTML list structures, which helps extract groups of high-quality items with abundant parallel information. Another is "Is-A'' relation extracted from knowledge bases, which is helpful to generate good questions with explicit prompts. To avoid introducing excessive noises, we design a relation selection process to filter out ineffective relations. We further design a BART-based model for generating clarification panes. The experimental results show that the structured information is good supplement for generating high-quality clarification panes.

Conversational search supports multi-turn user-system interactions to solve complex information needs. Compared with the traditional single-turn ad-hoc search, conversational search faces a more complex search intent understanding problem because a conversational search session is much longer and contains many noisy tokens. However, existing conversational dense retrieval solutions simply fine-tune the pre-trained ad-hoc query encoder on limited conversational search data, which are hard to achieve satisfactory performance in such a complex conversational search scenario. Meanwhile, the learned latent representation also lacks interpretability that people cannot perceive how the model understands the session. To tackle the above drawbacks, we propose a sparse Lexical-based Conversational REtriever (LeCoRE), which extends the SPLADE model with two well-matched multi-level denoising methods uniformly based on knowledge distillation and external query rewrites to generate denoised and interpretable lexical session representation. Extensive experiments on four public conversational search datasets in both normal and zero-shot evaluation settings demonstrate the strong performance of LeCoRE towards more effective and interpretable conversational search.

Users' search tasks have become increasingly complicated, requiring multiple queries and interactions with the results. Recent studies have demonstrated that modeling the historical user behaviors in a session can help understand the current search intent. Existing context-aware ranking models primarily encode the current session sequence (from the first behavior to the current query) and compute the ranking score using the high-level representations. However, there is usually some noise in the current session sequence (useless behaviors for inferring the search intent) that may affect the quality of the encoded representations. To help the encoding of the current user behavior sequence, we propose to use a decoder and the information of future sequences and a supplemental query. Specifically, we design three generative tasks that can help the encoder to infer the actual search intent: (1) predicting future queries, (2) predicting future clicked documents, and (3) predicting a supplemental query. We jointly learn the ranking task with these generative tasks using an encoder-decoder structured approach. Extensive experiments on two public search logs demonstrate that our model outperforms all existing baselines, and the designed generative tasks can actually help the ranking task. Besides, additional experiments also show that our approach can be easily applied to various Transformer-based encoder-decoder models and improve their performance.

Conversational search provides users with natural and convenient new search experience.Recently, conversational dense retrieval has shown to be a promising technique to realize conversational search.However, as conversational search systems have not been widely deployed, it is hard to get large-scale real conversational search sessions and relevance labels to support the training of conversational dense retrieval.To tackle this data scarcity problem, previous methods focus on developing better few-shot learning approaches or generating pseudo relevance labels, but the data they use for training still heavily rely on manual generation.In this paper, we present ConvTrans, a data augmentation method that can automatically transform easily-accessible web search sessions into conversational search sessions to fundamentally alleviate the data scarcity problem for conversational dense retrieval.ConvTrans eliminates the gaps between these two types of sessions in terms of session quality and query form to achieve effective session transformation.Extensive evaluations on two widely used conversational search benchmarks, i.e., CAsT-19 and CAsT-20, demonstrate that the same model trained on the data generated by Con-vTrans can achieve comparable retrieval performance as it trained on high-quality but expensive artificial conversational search data.